Naslund Chapter 1 Analysis
2023-02-10
Chapter 1 Sampling scheme
We sampled 4 sites over at least one 24 hr period. We installed 25 ebullition traps across 5 transects in each impoundment. Every 3 hours, we recorded the volume of gas accumulated in each ebullition trap and took a diffusive flux measurement immediately adjacent to 10 if the ebullition traps across the impoundment. To determine impoundment area, I digitized the impoundments using high resolution (0.5 ft) orthoimagery captured during the leaf off period. Pick has a beaver dam on the inlet stream and I delineated only to the beaver dam.
mapshot(
mapview(deans, col.regions = "#2D708EFF", alpha.regions = 1, legend = FALSE, homebutton=FALSE)+
mapview(catfish, col.regions = "#2D708EFF", alpha.regions = 1, legend = FALSE, homebutton=FALSE)+
mapview(sister3, col.regions = "#2D708EFF", alpha.regions = 1, legend = FALSE, homebutton=FALSE)+
mapview(pick, col.regions = "#2D708EFF", alpha.regions = 1, legend = FALSE, homebutton=FALSE),
file = "site_map.png",
remove_controls = c("zoomControl", "layersControl", "homeButton",
"drawToolbar", "easyButton"),
)| Site | Area (km2) | Max Depth (m) | Mean Depth (m) | Residence Time (days) | Dates Sampled |
|---|---|---|---|---|---|
| Deans | 0.0041 | 3.52 | 2.03 | 46 | 8/16/22 - 8/17/22 8/30/22 - 8/31/22 |
| Sister 3 | 0.0012 | 2.27 | 1.25 | 20 | 8/22/22 - 8/23/22 |
| Catfish | 0.0018 | 1.98 | 0.80 | 19 | 9/06/22 - 9/07/22 9/18/22 - 9/19/22 |
| Pick | 0.0077 | 3.80 | 1.58 | 45 | 8/22/22 - 8/23/22 |
1.1 Dissolved oxygen
We recorded dissolved oxygen every 15 minutes 0.25 m below the surface in every impoundment. From 6/20/22 to 7/13/22, we also recorded dissolved oxygen 0.1m from the bottom of Pick in the deepest location in the impoundment. Because dissolved oxygen was consistently 0 mg/L, we reallocated our oxygen sensors and installed both a top and bottom DO sensor in Sister 3.
1.3 PAR
All sensors were submerged ~0.25m below the water surface
1.3.1 Deans
Sensor casing cracked, so no available PAR data for second Deans sampling
dygraph(deans.xts) %>%
dyOptions(colors = "#DCE319FF") %>%
dyAxis("y", label = "PAR (umol/m2/s)")8/16/22
deans16.xts <- as.xts(par %>% filter(site == "Deans", date_time > ymd_hms("2022-08-16 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-08-17 09:50:00", tz = "America/New_York")) %>% pull(calibrated), order.by = par %>% filter(site == "Deans", date_time > ymd_hms("2022-08-16 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-08-17 09:50:00", tz = "America/New_York")) %>% pull(date_time))
dygraph(deans16.xts) %>%
dyOptions(colors = "#DCE319FF") %>%
dyAxis("y", label = "PAR (umol/m2/s)")1.3.2 Catfish
dygraph(catfish.xts) %>%
dyOptions(colors = "#55C667FF") %>%
dyAxis("y", label = "PAR (umol/m2/s)")9/6/22
catfish06_same.xts <-
as.xts(
par %>% filter(
site == "Catfish",
date_time > ymd_hms("2022-09-06 06:00:00", tz = "America/New_York"),
date_time < ymd_hms("2022-09-07 12:00:00", tz = "America/New_York")
) %>% mutate(
date_time_same = if_else(date == "06/09/2022 ", paste0("1900-01-01", time) %>% ymd_hms(tz = "America/New_York"), paste0("1900-01-02", time) %>% ymd_hms(tz = "America/New_York"))
) %>% pull(calibrated),
order.by = par %>% filter(
site == "Catfish",
date_time > ymd_hms("2022-09-06 06:00:00", tz = "America/New_York"),
date_time < ymd_hms("2022-09-07 12:00:00", tz = "America/New_York")
) %>% mutate(
date_time_same = if_else(date == "06/09/2022 ", paste0("1900-01-01", time) %>% ymd_hms(tz = "America/New_York"), paste0("1900-01-02", time) %>% ymd_hms(tz = "America/New_York"))
) %>% pull(date_time_same)
)
dygraph(catfish06_same.xts)catfish18_same.xts <-
as.xts(
par %>% filter(
site == "Catfish",
date_time > ymd_hms("2022-09-18 06:00:00", tz = "America/New_York"),
date_time < ymd_hms("2022-09-19 12:00:00", tz = "America/New_York")
) %>% mutate(
date_time_same = if_else(date == "18/09/2022 ", paste0("1900-01-01", time) %>% ymd_hms(tz = "America/New_York"), paste0("1900-01-02", time) %>% ymd_hms(tz = "America/New_York"))
) %>% pull(calibrated),
order.by = par %>% filter(
site == "Catfish",
date_time > ymd_hms("2022-09-18 06:00:00", tz = "America/New_York"),
date_time < ymd_hms("2022-09-19 12:00:00", tz = "America/New_York")
) %>% mutate(
date_time_same = if_else(date == "18/09/2022 ", paste0("1900-01-01", time) %>% ymd_hms(tz = "America/New_York"), paste0("1900-01-02", time) %>% ymd_hms(tz = "America/New_York"))
) %>% pull(date_time_same)
)
catfish_day.xts <- cbind(catfish06_same.xts, catfish18_same.xts)
pal <- c("#55C667FF", "#143C1B")
dygraph(catfish_day.xts) %>%
dyOptions(colors = pal) %>%
dyAxis("y", label = "PAR (umol/m2/s)")1.3.3 Pick
dygraph(pick.xts) %>%
dyOptions(colors = "#440154FF") %>%
dyAxis("y", label = "PAR (umol/m2/s)")#9/13/22
pick13.xts <- as.xts(par %>% filter(site == "Pick", date_time > ymd_hms("2022-09-13 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-09-14 12:00:00", tz = "America/New_York")) %>% pull(calibrated), order.by = par %>% filter(site == "Pick", date_time > ymd_hms("2022-09-13 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-09-14 12:00:00", tz = "America/New_York")) %>% pull(date_time))
dygraph(pick13.xts) %>%
dyOptions(colors = "#440154FF") %>%
dyAxis("y", label = "PAR (umol/m2/s)")1.3.4 Sister3
dygraph(sister3.xts) %>%
dyOptions(colors = "#2D708EFF") %>%
dyAxis("y", label = "PAR (umol/m2/s)")#8/22/22
sister322.xts <- as.xts(par %>% filter(site == "Sister3", date_time > ymd_hms("2022-08-22 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-08-23 12:00:00", tz = "America/New_York")) %>% pull(calibrated), order.by = par %>% filter(site == "Sister3", date_time > ymd_hms("2022-08-22 06:00:00", tz = "America/New_York"), date_time < ymd_hms("2022-08-23 12:00:00", tz = "America/New_York")) %>% pull(date_time))
dygraph(sister322.xts) %>%
dyOptions(colors = "#2D708EFF") %>%
dyAxis("y", label = "PAR (umol/m2/s)")1.4 Dissolved gas concentrations
I sampled for dissolved gas 0.25 m from the surface of the impoundments, 0.1m from the bottom, in the inlet stream, and in the outlet stream. The dissolved gas samples from Catfish on 9/18/22-9/19/22 were not handled properly, thus those values are excluded from the summaries.
gc <- read.csv("2-Clean Data/dissolved-gas.csv")
gc_summarized <- gc %>% mutate_if(is.character,
str_replace_all,
pattern = "Picks",
replacement = "Pick") %>%
mutate_if(is.character,
str_replace_all,
pattern = "oulet",
replacement = "outlet")%>%
group_by(Site, Collection.Date, Location) %>%
summarize(CH4_avg_umol = mean(Original_Liq_CH4_umol), CO2_avg_umol = mean(Original_Liq_CO2_umol), CH4_sd = sd(Original_Liq_CH4_umol, na.rm = T), CO2_sd = sd(Original_Liq_CO2_umol, na.rm = T), reps = n()) %>%
mutate(Location = factor(Location, levels = c("top","bottom", "inlet", "outlet")))## `summarise()` has grouped output by 'Site', 'Collection.Date'. You can override using the `.groups`
## argument.
gc_summarized %>% filter(mdy(Collection.Date)>mdy("08/15/22"), Collection.Date!="9/19/2022") %>% select(-CO2_avg_umol, -CO2_sd) %>% arrange(Location, desc(CH4_avg_umol))## # A tibble: 20 × 6
## # Groups: Site, Collection.Date [5]
## Site Collection.Date Location CH4_avg_umol CH4_sd reps
## <chr> <chr> <fct> <dbl> <dbl> <int>
## 1 Pick 9/13/2022 top 98.7 3.09 3
## 2 Deans 8/31/2022 top 0.980 1.32 3
## 3 Sister3 8/23/2022 top 0.849 0.0111 3
## 4 Catfish 9/6/2022 top 0.586 0.00180 3
## 5 Deans 8/16/2022 top 0.583 0.0476 2
## 6 Pick 9/13/2022 bottom 126. 60.7 3
## 7 Sister3 8/23/2022 bottom 2.89 0.985 3
## 8 Deans 8/16/2022 bottom 0.883 0.0543 3
## 9 Catfish 9/6/2022 bottom 0.557 0.0686 3
## 10 Deans 8/31/2022 bottom 0.290 0.0597 3
## 11 Deans 8/31/2022 inlet 16.9 4.77 3
## 12 Pick 9/13/2022 inlet 9.98 1.34 3
## 13 Deans 8/16/2022 inlet 9.95 2.86 3
## 14 Catfish 9/6/2022 inlet 0.598 0.197 3
## 15 Sister3 8/23/2022 inlet 0.0229 0.0000664 3
## 16 Deans 8/31/2022 outlet 4.31 0.936 3
## 17 Sister3 8/23/2022 outlet 2.62 0.0720 3
## 18 Catfish 9/6/2022 outlet 1.47 0.410 3
## 19 Pick 9/13/2022 outlet 0.365 0.170 3
## 20 Deans 8/16/2022 outlet 0.311 0.106 3
gc_summarized %>% filter(mdy(Collection.Date)>mdy("08/15/22"), Collection.Date!="9/19/2022") %>% select(-CH4_avg_umol, -CH4_sd) %>%arrange(Location, desc(CO2_avg_umol))## # A tibble: 20 × 6
## # Groups: Site, Collection.Date [5]
## Site Collection.Date Location CO2_avg_umol CO2_sd reps
## <chr> <chr> <fct> <dbl> <dbl> <int>
## 1 Pick 9/13/2022 top 560. 8.69 3
## 2 Catfish 9/6/2022 top 138. 5.69 3
## 3 Deans 8/16/2022 top 107. 3.11 2
## 4 Deans 8/31/2022 top 62.3 7.88 3
## 5 Sister3 8/23/2022 top 40.3 4.55 3
## 6 Pick 9/13/2022 bottom 635. 161. 3
## 7 Catfish 9/6/2022 bottom 164. 49.1 3
## 8 Deans 8/16/2022 bottom 88.9 16.4 3
## 9 Sister3 8/23/2022 bottom 70.0 15.2 3
## 10 Deans 8/31/2022 bottom 36.6 6.79 3
## 11 Deans 8/31/2022 inlet 377. 99.1 3
## 12 Deans 8/16/2022 inlet 234. 46.0 3
## 13 Pick 9/13/2022 inlet 168. 9.29 3
## 14 Catfish 9/6/2022 inlet 141. 6.10 3
## 15 Sister3 8/23/2022 inlet 75.2 7.54 3
## 16 Pick 9/13/2022 outlet 160. 25.6 3
## 17 Deans 8/31/2022 outlet 153. 44.1 3
## 18 Catfish 9/6/2022 outlet 142. 9.15 3
## 19 Sister3 8/23/2022 outlet 81.8 8.42 3
## 20 Deans 8/16/2022 outlet 50.2 5.51 3
1.5 Residence time calculation
I took a point estimate of discharge from the ponds while sampling, but these discharge values gave unrealistic residence times. I instead calculated discharge from a regional regression equation using average annual precipitation and watershed area (Gotvald et al. 2017).